Refining area of occupancy to address the modifiable areal unit problem in ecology and conservation

The ‘modifiable areal unit problem’ is prevalent across many aspects of spatial analysis within ecology and conservation. The problem is particularly manifest when calculating metrics for extinction risk estimation, for example, area of occupancy (AOO).Although embedded into the International Union for the Conservation of Nature (IUCN) Red List criteria, AOO is often not used or is poorly applied. Here we evaluate new and existing methods for calculating AOO from occurrence records and present a method for determining the minimum AOO using a uniform grid. We evaluate the grid cell shape, grid origin and grid rotation with both real-world and simulated data, reviewing the effects on AOO values, and possible impacts for species already assessed on the IUCN Red List. We show that AOO can vary by up to 80% and a ratio of cells to points of 1:1.21 gives the maximum variation in the number of occupied cells. These findings potentially impact 3% of existing species on the IUCN Red List, as well as species not yet assessed. We show that a new method that combines both grid rotation and moving grid origin gives fast, robust and reproducible results and, in the majority of cases, achieves the minimum AOO. As well as reporting minimum AOO, we outline a confidence interval which should be incorporated in to existing tools that support species risk assessment. We also make further recommendations for reporting AOO and other areal measurements within ecology, leading to more robust methods for future species risk assessment

Impact of alternative metrics on estimates of extent of occurrence for extinction risk assessment

In International Union for Conservation of Nature (IUCN) Red List assessments, extent of occurrence (EOO) is a key measure of extinction risk. However, the way assessors estimate EOO from maps of species’ distributions is inconsistent among assessments of different species and among major taxonomic groups. Assessors often estimate EOO from the area of mapped distribution, but these maps often exclude areas that are not habitat in idiosyncratic ways and are not created at the same spatial resolutions. We assessed the impact on extinction risk categories of applying different methods (minimum convex polygon, alpha hull) for estimating EOO for 21,763 species of mammals, birds, and amphibians. Overall, the percentage of threatened species requiring down listing to a lower category of threat (taking into account other Red List criteria under which they qualified) spanned 11–13% for all species combined (14–15% for mammals, 7–8% for birds, and 12–15% for amphibians). These down listings resulted from larger estimates of EOO and depended on the EOO calculation method. Using birds as an example, we found that 14% of threatened and near threatened species could require down listing based on the minimum convex polygon (MCP) approach, an approach that is now recommended by IUCN. Other metrics (such as alpha hull) had marginally smaller impacts. Our results suggest that uniformly applying the MCP approach may lead to a one-time down listing of hundreds of species but ultimately ensure consistency across assessments and realign the calculation of EOO with the theoretical basis on which the metric was founded

Automated conservation assessment of the orchid family with deep learning

International Union for Conservation of Nature (IUCN) Red List assessments are essential for prioritizing conservation needs but are resource intensive and therefore available only for a fraction of global species richness. Automated conservation assessments based on digitally available geographic occurrence records can be a rapid alternative, but it is unclear how reliable these assessments are. We conducted automated conservation assessments for 13,910 species (47.3% of the known species in the family) of the diverse and globally distributed orchid family (Orchidaceae), for which most species (13,049) were previously unassessed by IUCN. We used a novel method based on a deep neural network (IUC‐NN). We identified 4,342 orchid species (31.2% of the evaluated species) as possibly threatened with extinction (equivalent to IUCN categories critically endangered [CR], endangered [EN], or vulnerable [VU]) and Madagascar, East Africa, Southeast Asia, and several oceanic islands as priority areas for orchid conservation. Orchidaceae provided a model with which to test the sensitivity of automated assessment methods to problems with data availability, data quality, and geographic sampling bias. The IUC‐ NN identified possibly threatened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias relative to the IUCN Red List and was robust even when data availability was low and there were geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in identifying species at the greatest risk of extinction